Replicate
nexu-io/open-design
Discover, compare, and run AI models using Replicate's API. An agent skill from nexu-io/open-design.
Agent skill
Siggraph Artifact Evaluation
by brycewang-stanford in brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when pursuing the Graphics Replicability Stamp (GRSI) or Code Replicability in Computer Graphics (CRCG) recognition for an accepted SIGGRAPH / TOG paper, covering how…
MITAuto-check passed
Install Siggraph Artifact Evaluation
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill siggraph-artifact-evaluation -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills siggraph-artifact-evaluation --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/SIGGRAPH-Skills/skills/siggraph-artifact-evaluation .claude/skills/siggraph-artifact-evaluation && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "siggraph-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGGRAPH-Skills/skills/siggraph-artifact-evaluation into .claude/skills/siggraph-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "siggraph-artifact-evaluation", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGGRAPH-Skills/skills/siggraph-artifact-evaluationType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill siggraph-artifact-evaluation -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills siggraph-artifact-evaluation --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/SIGGRAPH-Skills/skills/siggraph-artifact-evaluation .agents/skills/siggraph-artifact-evaluation && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "siggraph-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGGRAPH-Skills/skills/siggraph-artifact-evaluation into .agents/skills/siggraph-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "siggraph-artifact-evaluation", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill siggraph-artifact-evaluation -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills siggraph-artifact-evaluation --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/SIGGRAPH-Skills/skills/siggraph-artifact-evaluation .cursor/skills/siggraph-artifact-evaluation && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "siggraph-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGGRAPH-Skills/skills/siggraph-artifact-evaluation into .cursor/skills/siggraph-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "siggraph-artifact-evaluation", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/brycewang-stanford/Awesome-Journal-Skills.git --path SIGGRAPH-Skills/skills/siggraph-artifact-evaluation--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill siggraph-artifact-evaluation -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills siggraph-artifact-evaluation --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/SIGGRAPH-Skills/skills/siggraph-artifact-evaluation .gemini/skills/siggraph-artifact-evaluation && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "siggraph-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGGRAPH-Skills/skills/siggraph-artifact-evaluation into .gemini/skills/siggraph-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "siggraph-artifact-evaluation", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills siggraph-artifact-evaluationInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill siggraph-artifact-evaluation -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/SIGGRAPH-Skills/skills/siggraph-artifact-evaluation .github/skills/siggraph-artifact-evaluation && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "siggraph-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGGRAPH-Skills/skills/siggraph-artifact-evaluation into .github/skills/siggraph-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "siggraph-artifact-evaluation", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill siggraph-artifact-evaluation -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills siggraph-artifact-evaluation --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/SIGGRAPH-Skills/skills/siggraph-artifact-evaluation .opencode/skills/siggraph-artifact-evaluation && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "siggraph-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGGRAPH-Skills/skills/siggraph-artifact-evaluation into .opencode/skills/siggraph-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "siggraph-artifact-evaluation", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
siggraph-artifact-evaluation- GitHub stars
- 1.2k
- Token cost
- ~1.5k tokens
- SKILL.md length
- 541 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when pursuing the Graphics Replicability Stamp (GRSI) or Code Replicability in Computer Graphics (CRCG) recognition for an accepted SIGGRAPH / TOG paper, covering how…
- Pursuing the Graphics Replicability Stamp (GRSI)
- SKILL.md covers What the stamp is (and is not), What a volunteer actually does, Packaging plan for a… and Determinism is the…, plus 2 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Code Replicability in Computer Graphics (CRCG) recognition for an accepted SIGGRAPH / TOG paper
What it does
Siggraph Artifact Evaluation is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when pursuing the Graphics Replicability Stamp (GRSI) or Code Replicability in Computer Graphics (CRCG) recognition for an accepted SIGGRAPH / TOG paper, covering how graphics replicability differs from ACM artifact badging, what volunteers actually run, deterministic result reproduction, Software Heritage archiving, and the separate post-acceptance timing.
Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
The repository describes itself as: Journal-specific Claude Code/Codex skill packs covering mainstream journals — AER, QJE, Nature, Cell, 管理世界, 经济研究 & 200+ more — your fast track to getting published. ｜ 覆盖主流期刊的… The licence is MIT.
When your agent uses it
- Pursuing the Graphics Replicability Stamp (GRSI)
- Code Replicability in Computer Graphics (CRCG) recognition for an accepted SIGGRAPH / TOG paper
- Covering how graphics replicability differs from ACM artifact badging
- What volunteers actually run
Example prompts
- “/siggraph-artifact-evaluation”
Requirements
- Docker
What it can do on your machine
Read from SKILL.md and the folder at commit 932eb23. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Siggraph Artifact Evaluation loads about 1.5k tokens when it runs. Until then it costs about 98 tokens; SKILL.md has 541 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from brycewang-stanford/Awesome-Journal-Skills at commit 932eb23, republished under its MIT licence (© brycewang-stanford). 541 words, ~1,489 tokens.
Download SKILL.mdSave it as
.claude/skills/siggraph-artifact-evaluation/SKILL.md (or your agent's skills folder).- name
- siggraph-artifact-evaluation
- description
- Use when pursuing the Graphics Replicability Stamp (GRSI) or Code Replicability in Computer Graphics (CRCG) recognition for an accepted SIGGRAPH / TOG paper, covering how graphics replicability differs from ACM artifact badging, what volunteers actually run, deterministic result reproduction, Software Heritage archiving, and the separate post-acceptance timing.
SIGGRAPH Artifact Evaluation (Replicability Stamp)
SIGGRAPH does not run the ACM Artifact Review and Badging scheme that SIGSOFT and systems
venues use. The computer-graphics community's equivalent is a community-run replicability
stamp, earned after acceptance and independent of the review: the Graphics Replicability
Stamp Initiative (GRSI, replicabilitystamp.org) and the related Code Replicability in Computer
Graphics (CRCG, replicability.graphics). The distinction matters: a stamp certifies that a
volunteer rebuilt your code and reproduced your paper's results, and archives that code for the
community. Facts below trace to resources/official-source-map.md; confirm the current GRSI
process before you package.
What the stamp is (and is not)
| Graphics Replicability Stamp (GRSI) | ACM Artifact Badges (other venues) | |
|---|---|---|
| Who runs it | Volunteer researchers from the graphics community | The venue's artifact evaluation committee |
| What it certifies | Your code replicates the paper's results | Available / Functional / Reusable / Reproduced |
| When | Post-acceptance, on the initiative's own schedule | Post-acceptance, on the venue's AE deadline |
| Archiving | Software Heritage snapshot + long-term ID (since 2023) | DOI-issuing archive |
| Scope | Independent of SIGGRAPH's review; optional recognition | Tied to the venue's AE track |
The GRSI has recognized graphics code since 2016 and is supported across the field's venues (ACM TOG, IEEE TVCG, Wiley CGF, Elsevier C&G, CAD/CAGD). CRCG has, since July 2020, focused specifically on checking whether SIGGRAPH papers' results are replicable. Neither is required for publication — but the stamp is the credible, checkable signal of a runnable contribution in this community.
What a volunteer actually does
Assume a graphics-literate volunteer clones your repository on their own machine and tries to regenerate a headline result from your paper. They are checking replicability of results, not just that the code compiles:
| Contribution type | What the volunteer reproduces | Common failure caught |
|---|---|---|
| A rendering technique | A converged image matching a paper figure, within tolerance | Non-deterministic output; missing scene assets |
| A geometry/mesh method | A processed mesh matching the reported statistics | Hard-coded absolute paths; unshipped input meshes |
| A simulation | A representative frame/sequence from the paper | Unseeded RNG; platform-specific solver drift |
| A learning-based method | A result from released weights, not retraining from scratch | Weights absent; inference needs undocumented data |
Design so the first headline result reproduces from a documented command on a clean checkout.
Show full SKILL.md (184 more words)Show less
Packaging plan for a replicable graphics artifact
[Build] a documented, pinned build (CMake/conda/Docker) that compiles on a clean machine;
name exact compiler/CUDA/driver versions where GPU code is involved
[Assets] ship (or give a stable download for) the scenes, meshes, textures, and weights the
results need -- code without inputs cannot replicate a figure
[Determinism] fix seeds; document tolerance for floating-point/GPU non-determinism; state which
results are bit-exact vs perceptually-equal
[Mapping] a table: paper figure/table -> command -> expected output image/metric/frame
[Timings] report the hardware and the wall-clock the paper claims, so timings are checkable
[License] an OSI-approved license so the code can be shared and reused
[Archive] a Software Heritage snapshot (GRSI archives accepted code there) + a Zenodo DOIDeterminism is the graphics-specific hard part
Reproducing an image is not reproducing a number. Plan for it:
- State the comparison metric and tolerance. Volunteers cannot judge "looks right" — give PSNR/SSIM/LPIPS or a diff threshold against a bundled reference image.
- Pin the stochastic pieces. Monte Carlo renderers, stochastic simulations, and GPU reductions drift; seed them and document the residual non-determinism.
- Bundle reference outputs. Include the exact images/frames the paper reports so a volunteer compares against ground truth, not a re-render of their own.
- Separate "replicate the result" from "retrain the model." For learning-based work, ship weights and an inference path; full retraining is rarely the replication target.
Calibration
- The stamp is post-acceptance and optional; it is decided by the initiative's volunteers on their timeline, not by the SIGGRAPH committee, and not on the camera-ready deadline.
- GRSI and CRCG are related but distinct efforts; check each initiative's current submission instructions and supported-venue list before submitting your code.
- A stamp strengthens a paper's reach but is never a substitute for in-paper evidence — the review already happened without it.
Output format
[Target] Graphics Replicability Stamp (GRSI) / CRCG check / both
[Headline result] <figure/table the volunteer will reproduce>
[Clean-machine build] compiles + runs from documented command? yes/no
[Assets] scenes/meshes/weights shipped or stably linked? yes/no
[Determinism] seeds fixed + tolerance + reference outputs bundled? yes/no
[Claim mapping] <figure -> command -> expected output present?>
[Archive] Software Heritage snapshot + DOI + OSI license? yes/no
[Fixes before submitting code] <ordered>© brycewang-stanford, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in SIGGRAPH-Skills/skills/siggraph-artifact-evaluation of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Siggraph Artifact Evaluation next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Siggraph Artifact Evaluation this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.5k | Automated safety check: Pass | MIT | |
| Replicatenexu-io/open-design | 100k | — | ~295 | Automated safety check: Pass | Apache-2.0 | |
| Arize Evaluatorgithub/awesome-copilot | 40k | 1 repos | ~8.1k | Automated safety check: Notes | MIT | |
| Artifacts Buildernexu-io/open-design | 100k | — | ~347 | Automated safety check: Pass | Apache-2.0 | |
| LLM Evaluationdavila7/claude-code-templates | 33k | 12 repos | ~3.5k | Automated safety check: Pass | MIT | |
| Web Artifacts Builderanthropics/skills | 180k | 40 repos | ~769 | Automated safety check: Pass | Apache-2.0 |
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Questions about Siggraph Artifact Evaluation
What does Siggraph Artifact Evaluation do?
A skill your agent uses when pursuing the Graphics Replicability Stamp (GRSI) or Code Replicability in Computer Graphics (CRCG) recognition for an accepted SIGGRAPH / TOG paper, covering how…. Siggraph Artifact Evaluation is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when pursuing the Graphics Replicability Stamp (GRSI) or Code Replicability in Computer Graphics (CRCG) recognition for an accepted SIGGRAPH / TOG paper, covering how graphics replicability differs from ACM artifact badging, what volunteers actually run, deterministic result reproduction, Software Heritage archiving, and the separate post-acceptance timing.
When should I use Siggraph Artifact Evaluation?
Siggraph Artifact Evaluation fits situations like: pursuing the Graphics Replicability Stamp (GRSI); code Replicability in Computer Graphics (CRCG) recognition for an accepted SIGGRAPH / TOG paper; covering how graphics replicability differs from ACM artifact badging; what volunteers actually run.
How do I install Siggraph Artifact Evaluation in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill siggraph-artifact-evaluation -a claude-code`. Or copy the skill folder (SIGGRAPH-Skills/skills/siggraph-artifact-evaluation in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/siggraph-artifact-evaluation in your project. Claude Code loads it when a task matches its description.
How do I install Siggraph Artifact Evaluation in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill siggraph-artifact-evaluation -a codex`. Or copy the skill folder (SIGGRAPH-Skills/skills/siggraph-artifact-evaluation in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/siggraph-artifact-evaluation in your project. Codex loads it when a task matches its description.
Can I use Siggraph Artifact Evaluation in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill siggraph-artifact-evaluation -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/siggraph-artifact-evaluation, .gemini/skills/siggraph-artifact-evaluation, .github/skills/siggraph-artifact-evaluation and .opencode/skills/siggraph-artifact-evaluation in your project.
What does Siggraph Artifact Evaluation need to run?
SKILL.md names no scripts, command-line tools or credentials: Siggraph Artifact Evaluation is instructions for the agent only. Our summary lists: Docker.
Does Siggraph Artifact Evaluation access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Siggraph Artifact Evaluation safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Siggraph Artifact Evaluation use?
Siggraph Artifact Evaluation is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Siggraph Artifact Evaluation use?
About 1.5k tokens (SKILL.md is roughly 6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Siggraph Artifact Evaluation?
Skills that share tags, products or a category with Siggraph Artifact Evaluation: Replicate (nexu-io/open-design, 100k stars), Arize Evaluator (github/awesome-copilot, 40k stars), Artifacts Builder (nexu-io/open-design, 100k stars) and LLM Evaluation (davila7/claude-code-templates, 33k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Siggraph Artifact Evaluation?
brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,231 GitHub stars. The repository holds 2,387 skills in this directory. The repository was last updated on September 27, 2026.
Source: brycewang-stanford/Awesome-Journal-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.